find-boundaries

find_boundaries

skimage.segmentation.find_boundaries(label_img, connectivity=1, mode='thick', background=0) [source]

Return bool array where boundaries between labeled regions are True.

Parameters:

label_img : array of int or bool An array in which different regions are labeled with either different integers or boolean values. connectivity: int in {1, ..., `label_img.ndim`}, optional A pixel is considered a boundary pixel if any of its neighbors has a different label. connectivity controls which pixels are considered neighbors. A connectivity of 1 (default) means pixels sharing an edge (in 2D) or a face (in 3D) will be considered neighbors. A connectivity of label_img.ndim means pixels sharing a corner will be considered neighbors. mode: string in {‘thick’, ‘inner’, ‘outer’, ‘subpixel’} How to mark the boundaries: thick: any pixel not completely surrounded by pixels of the same label (defined by connectivity) is marked as a boundary. This results in boundaries that are 2 pixels thick. inner: outline the pixels just inside of objects, leaving background pixels untouched. outer: outline pixels in the background around object boundaries. When two objects touch, their boundary is also marked. subpixel: return a doubled image, with pixels between the original pixels marked as boundary where appropriate. background: int, optional For modes ‘inner’ and ‘outer’, a definition of a background label is required. See mode for descriptions of these two.

Returns:

boundaries : array of bool, same shape as label_img A bool image where True represents a boundary pixel. For mode equal to ‘subpixel’, boundaries.shape[i] is equal to 2 * label_img.shape[i] - 1 for all i (a pixel is inserted in between all other pairs of pixels).

Examples

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>>> labels = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], ...                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], ...                    [0, 0, 0, 0, 0, 5, 5, 5, 0, 0], ...                    [0, 0, 1, 1, 1, 5, 5, 5, 0, 0], ...                    [0, 0, 1, 1, 1, 5, 5, 5, 0, 0], ...                    [0, 0, 1, 1, 1, 5, 5, 5, 0, 0], ...                    [0, 0, 0, 0, 0, 5, 5, 5, 0, 0], ...                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], ...                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=np.uint8) >>> find_boundaries(labels, mode='thick').astype(np.uint8) array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],        [0, 0, 0, 0, 0, 1, 1, 1, 0, 0],        [0, 0, 1, 1, 1, 1, 1, 1, 1, 0],        [0, 1, 1, 1, 1, 1, 0, 1, 1, 0],        [0, 1, 1, 0, 1, 1, 0, 1, 1, 0],        [0, 1, 1, 1, 1, 1, 0, 1, 1, 0],        [0, 0, 1, 1, 1, 1, 1, 1, 1, 0],        [0, 0, 0, 0, 0, 1, 1, 1, 0, 0],        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8) >>> find_boundaries(labels, mode='inner').astype(np.uint8) array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],        [0, 0, 0, 0, 0, 1, 1, 1, 0, 0],        [0, 0, 1, 1, 1, 1, 0, 1, 0, 0],        [0, 0, 1, 0, 1, 1, 0, 1, 0, 0],        [0, 0, 1, 1, 1, 1, 0, 1, 0, 0],        [0, 0, 0, 0, 0, 1, 1, 1, 0, 0],        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8) >>> find_boundaries(labels, mode='outer').astype(np.uint8) array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],        [0, 0, 0, 0, 0, 1, 1, 1, 0, 0],        [0, 0, 1, 1, 1, 1, 0, 0, 1, 0],        [0, 1, 0, 0, 1, 1, 0, 0, 1, 0],        [0, 1, 0, 0, 1, 1, 0, 0, 1, 0],        [0, 1, 0, 0, 1, 1, 0, 0, 1, 0],        [0, 0, 1, 1, 1, 1, 0, 0, 1, 0],        [0, 0, 0, 0, 0, 1, 1, 1, 0, 0],        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8) >>> labels_small = labels[::2, ::3] >>> labels_small array([[0, 0, 0, 0],        [0, 0, 5, 0],        [0, 1, 5, 0],        [0, 0, 5, 0],        [0, 0, 0, 0]], dtype=uint8) >>> find_boundaries(labels_small, mode='subpixel').astype(np.uint8) array([[0, 0, 0, 0, 0, 0, 0],        [0, 0, 0, 1, 1, 1, 0],        [0, 0, 0, 1, 0, 1, 0],        [0, 1, 1, 1, 0, 1, 0],        [0, 1, 0, 1, 0, 1, 0],        [0, 1, 1, 1, 0, 1, 0],        [0, 0, 0, 1, 0, 1, 0],        [0, 0, 0, 1, 1, 1, 0],        [0, 0, 0, 0, 0, 0, 0]], dtype=uint8) >>> bool_image = np.array([[False, False, False, False, False], ...                        [False, False, False, False, False], ...                        [False, False,  True,  True,  True], ...                        [False, False,  True,  True,  True], ...                        [False, False,  True,  True,  True]], dtype=np.bool) >>> find_boundaries(bool_image) array([[False, False, False, False, False],        [False, False,  True,  True,  True],        [False,  True,  True,  True,  True],        [False,  True,  True, False, False],        [False,  True,  True, False, False]], dtype=bool)